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Auxiliary Learning

Auxiliary learning aims to find or design auxiliary tasks which can improve the performance on one or some primary tasks.

( Image credit: Self-Supervised Generalisation with Meta Auxiliary Learning )

Papers

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Showing 31–40 of 100 papers

TitleStatusHype
Hierarchical Auxiliary Learning—0
Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks—0
Enhancing Deep Knowledge Tracing with Auxiliary Tasks—0
Embracing the Disharmony in Medical Imaging: A Simple and Effective Framework for Domain Adaptation—0
Behavior Importance-Aware Graph Neural Architecture Search for Cross-Domain Recommendation—0
Enhancing Sequential Recommendation with Graph Contrastive Learning—0
Entire Space Counterfactual Learning: Tuning, Analytical Properties and Industrial Applications—0
Fast Adaptation for Human Pose Estimation via Meta-Optimization—0
Federated Learning with Server Learning: Enhancing Performance for Non-IID Data—0
Auxiliary Tasks Enhanced Dual-affinity Learning for Weakly Supervised Semantic Segmentation—0
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